The Effect of Soil Sampling Density and Spatial Autocorrelation on Interpolation Accuracy of Chemical Soil Properties in Arable Cropland

Abstract

Knowledge of the relationship between soil sampling density and spatial autocorrelation with interpolation accuracy allows more time- and cost-efficient spatial analysis. Previous studies produced contradictory observations regarding this relationship, and this study aims to determine and explore under which conditions the interpolation accuracy of chemical soil properties is affected. The study area covered 823.4 ha of agricultural land with 160 soil samples containing phosphorus pentoxide (P2O5) and potassium oxide (K2O) values. The original set was split into eight subsets using a geographically stratified random split method, interpolated using the ordinary kriging (OK) and inverse distance weighted (IDW) methods. OK and IDW achieved similar interpolation accuracy regardless of the soil chemical property and sampling density, contrary to the majority of previous studies which observed the superiority of kriging as a deterministic interpolation method. The primary dependence of interpolation accuracy to soil sampling density was observed, having R2 in the range of 56.5–83.4% for the interpolation accuracy assessment. While this study enables farmers to perform efficient soil sampling according to the desired level of detail, it could also prove useful to professions dependent on field sampling, such as biology, geology, and mining.

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

“…Due to its deterministic nature, IDW was resistant to this property, with its interpolation accuracy ranging from 0.233 to 0.405 for the P 2 O 5 and from 0.234 to 0.374 for the K 2 O. It generally produced a lower accuracy but with a more balanced approach regarding sensitivity to the input values, as noted in [29]. Besides varying the interpolation accuracy, the resulting value ranges and CV values were severely affected by the selection of the interpolation method and its parameters.…”

“…De Zorzi et al [28] stated that the spatial distribution of soil sampling depends on the area and geometrical properties of the agricultural parcel. The goal of the soil sampling is to collect the smallest possible number of representative samples that will not impair the accuracy of the spatial interpolation [29]. For this purpose, it is common to conduct preliminary zoning of agricultural plots before sampling, primarily by scanning the electrical conductivity of the soil.…”

“…This is a costly, labor-and time-expensive procedure, which disregards one of the purposes of precision agriculture. Furthermore, Radočaj et al [29] proved that soil sampling density was a primary indicator of soil prediction accuracy using conventional interpolation methods. Mirás-Avalos et al [33] observed a proportionally lower prediction accuracy of kriging with a larger distance from the nearest soil sample, resulting in unrealistic smoothing due to extrapolation, which is exaggerated for agricultural parcels with non-regular shapes.…”

“…The reliability of this procedure particularly occurs for the high number of heterogenous soil sampling values. The split-sample method evaluates the prediction accuracy and robustness of the interpolation methods by splitting the input data into two parts based on a predefined ratio, forming training and test data [29]. The iteration of the procedure and repetitive accuracy assessment ensures the evaluation of consistency and robustness of the selected interpolation method.…”

“…Phosphorous pentoxide (P 2 O 5 ) and potassium oxide (K 2 O) were used for the prediction, representing two of the most important soil properties in agricultural fertilization [96]. A total of 121 samples were used in the agricultural parcel of 4.1 km 2 , representing a micro-location such as in [29,44]. The descriptive statistics of the input soil sample set is presented in Table 3.…”